Parasight diagnostic platform can detect malaria and ID the species in less than 4 minutes, using machine learning and computer vision.

In 2015, about 212 million people were infected with malaria and an estimated 429,000 died from the disease, which is transmitted by mosquito-borne parasites. As Israeli researchers investigate solutions for preventing and treating malaria, an Israeli company founded in 2010 has developed a device that helps labs around the world detect malaria rapidly, reliably and automatically using unique computer vision-based algorithms for identifying blood-related diseases.

About 70 percent of the 500 million malaria tests done annually are performed using manual microscopy, which is slow and only as reliable as the person processing the test, explains Sight Diagnostics CEO Yossi Pollak. The other 30% are done with rapid tests that are not sufficiently accurate.

“It was pretty clear everywhere we went that current solutions are inadequate, and that a ‘high-tech’ solution is needed,” says Pollak. That solution is a diagnostic platform that operates on microscopy images of human blood.

Sight’s first product, the Parasight malaria detection device, completes malaria tests in less than four minutes with more than 99% accuracy for sensitivity and specificity. It also identifies the malaria species and counts the exact number of parasites per microliter. Results are automatically uploaded to the lab information system.

“The underlying technology is machine learning and computer vision, so this kind of system improves with the expansion of our database,”says Pollak. “Our devices performed several hundred thousand tests in the field over the past year, allowing us to upload interesting samples to our servers and improve the machine-learning system to be even more accurate and detect earlier.”

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